EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

News

image of prof. David Miller
February 2021

Professor David Miller joins Professor Russ Altman on The Future of Everything podcast. David's research interests include the use of optics in switching, interconnection, communications, computing, and sensing systems, physics and applications of quantum well optics and optoelectronics, and fundamental features and limits for optics and nanophotonics in communications and information processing. 

In this podcast he explains the remarkable potential of using light instead of electricity in computation.

 

"A silicon chip these days looks like six Manhattan grids stacked atop one another," Miller says of the challenge facing today's technology. Photonics holds the promise of more powerful computing by beaming tiny packets of photons through light-bearing conduits that carry 100,000 times more data than today's comparable wires, and it can do it using far less energy, too.

Before that day can arrive, however, Miller says photonic components need to become much smaller and less expensive to compete with the sheer scale advantages silicon enjoys, and that will require investment. But, for once, a way forward is there for the asking, as Miller tells bioengineer Russ Altman, host of Stanford Engineering's The Future of Everything podcast. Listen and subscribe here.

image of our exemplary staff*
February 2021

Congratulations to Beverly Davis, John DeSilva, Kenny Green, Helen Niu, and Lisa Sickorez! They received nominations from faculty, staff and students, who appreciate their commitment and willingness to go above and beyond the ordinary! Excerpts from the nominations are below.

The staff gift card recipients make profound and positive impact in the electrical engineering department's everyday work and academic environment.
Please join us in congratulating them.

Beverly Davis

  • Beverly leads with efficiency and administrative experience.
  • She lights up our office meetings with her exuberance and kindness; making our lab group a warm and welcoming community.

John DeSilva

  • John is a "super-facilitator"!
  • He is proactive and keeps an eye our for things that need to be done in the office, whether or not related to IT. He is always willing to say 'yes'.

Kenny Green

  • He's always very responsive whenever we have any issue with our lab or office space. Glad I get to work with him!
  • Kenny's always very prompt and knowledgeable, not to mention an all around funny and nice guy!

Helen Niu

  • Once Helen undertakes a task, one can always be sure the task will be completed correctly and in time.
  • She is a thorough, knowledgeable and responsible individual.

Lisa Sickorez

  • No doubt Lisa is the hardest working person in EE! She doesn't let anything fall through the cracks.
  • Lisa monitors a great deal of department business and keeps all 120+ of us on track. I really appreciate her proactivity and thoughtfulness.

 image of our exemplary staff!*

*All photos were taken from our photo library and are pre-Covid.

The Staff Gift Card Bonus Program is sponsored by the School of Engineering. Each year, the EE department receives several gift cards to distribute to staff members who have been recognized for going above and beyond their role. Staff are chosen from nominations received from faculty, students, and staff. Past nominations are eligible for future months.

Consider nominating a deserving staff person, or group, today. Each recipient receives a $50 Amazon gift card. Nominations can be made at any time. There are no restrictions on the quantity, persons or groups that you can nominate. Submitters are asked to include a citation of how the group or person went above and beyond. The submitter can choose to remain anonymous.

Nominate a deserving colleague today.

image of prof Hennessy
February 2021
Professor John Hennessy and Professor David Patterson (University of California, Berkeley) have received the BBVA Frontiers of Knowledge Award in Information and Communications Technologies. Their citation reads, "for turning computer architecture into a science and designing the processors that power today’s devices. […] They conceived the scientific field of computer architecture, motivated a systematic and quantitative design approach to system performance, created a style of reduced instruction set processors that has transformed how industry builds computer systems, and have made transformative advancements in computer reliability and in large-scale system coherence”. 
 
“Professors John Hennessy and David Patterson are synonymous with the inception and formalization of this field,” the citation reads. “Before their work, the design of computers – and in particular the measurement of computer performance – was more of an art than a science, and practitioners lacked a set of repeatable principles to conceptualize and evaluate computer designs. Patterson and Hennessy provided, for the first time, a conceptual framework that gave the field a grounded approach towards measuring a computer’s performance, energy efficiency, and complexity.”

The new laureates’ scientific contributions had their didactic parallel in a landmark textbook, Computer Architecture: A Quantitative Approach, which three decades on from its first release and after six editions with regularly updated content, is still considered “the bible” for the discipline in universities around the world.
 
 
Please join us in congratulating John on his extraordinary contributions to teaching, industry, and innovation.
 
 
 
Excerpted from: BBVA Foundation Frontiers of Knowledge Awards, February 2021.

image of prof. Kunle Olukotun
February 2021

Professor Kunle Olukotun has been elected to the National Academy of Engineering, "for contributions to on-chip multiprocessor architectures and advancement to commercial realization." Kunle will be formally inducted during the NAE's annual meeting on October 3.

Election to the National Academy of Engineering is among the highest professional distinctions accorded to an engineer. Academy membership honors those who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature" and to "the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education."

Hearty congratulations to Kunle on this well-deserved recognition!

 

Read National Academy of Engineering Press Release

image of prof James Zou and PhD Amirata Ghorbani
February 2021

Each of us continuously generates a stream of data. When we buy a coffee, watch a romcom or action movie, or visit the gym or the doctor's office (tracked by our phones), we hand over our data to companies that hope to make money from that information – either by using it to train an AI system to predict our future behavior or by selling it to others.

But what is that data worth?

"There's a lot of interest in thinking about the value of data," says Professor James Zou, member of the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and faculty lead of a new HAI executive education program on the subject. How should companies set prices for data they buy and sell? How much does any given dataset contribute to a company's bottom line? Should each of us receive a data dividend when companies use our data?

Motivated by these questions, James and graduate student Amirata Ghorbani have developed a new and principled approach to calculating the value of data that is used to train AI models. Their approach, detailed in a paper presented at the International Conference on Machine Learning and summarized for a slightly less technical audience in arXiv, is based on a Nobel Prize-winning economics method and improves upon existing methods for determining the worth of individual datapoints or datasets. In addition, it can help AI systems designers identify low value data that should be excluded from AI training sets as well as high value data worth acquiring. It can even be used to reduce bias in AI systems.

[...]

The data Shapley value can even be used to reduce the existing biases in datasets. For example, many facial recognition systems are trained on datasets that have more images of white males than minorities or women. When these systems are deployed in the real world, their performance suffers because they see more diverse populations. To address this problem, James and Amirata ran an experiment: After a facial recognition system had been deployed in a real setting, they calculated how much each image in the training set contributed to the model's performance in the wild. They found that the images of minorities and women had the highest Shapley values and the images of white males had the lowest Shapley values. They then used this information to fix the problem – weighting the training process in favor of the more valuable images. "By giving those images higher value and giving them more weight in the training process, the data Shapley value will actually make the algorithm work better in deployment – especially for minority populations," James says.

 

Excerpted from: HAI "Quantifying the Value of Data"

image of prof Shanhui Fan
February 2021

Professor Shanhui Fan and his team have developed a wireless charging system that could transmit electricity even as the distance to the receiver changes. They have incorporated an amplifier and feedback resistor that allows the system to automatically adjust its operating frequency as the distance between the charger and the moving object changes.

By replacing their original amplifier with a far more efficient switch mode amplifier, they boosted efficiency. The latest iteration can wirelessly transmit 10 watts of electricity over a distance of 2 or 3 feet.

Shanhui says there aren't any fundamental obstacles to scaling up a system to transmit the tens or hundreds of kilowatts that a car would need. In fact, he claims the system is more than fast enough to resupply a speeding automobile.

 

Excerpted from "Engineers Race to Develop Wireless Charging Technology"

image of Chuan-Zheng Lee, EE PhD candidate
February 2021

Congratulations to Chuan-Zheng Lee (PhD candidate) and Leighton Pate Barnes (PhD candidate) on receiving the IEEE GLOBECOM 2020 Selected Areas of Communications Symposium Best Paper Award. Their paper is titled "Over-the-Air Statistical Estimation." Professor Ayfer Özgür is their advisor and co-author.

The award was presented by the IEEE GLOBECOM 2020 Awards Committee and IEEE GLOBECOM 2020 Organizing Committee.

 

Please join us in congratulating Ayfer, Chuan-Zheng, and Leighton on receiving this prestigious best paper award!

IEEE Global Communications Conference (GLOBECOM) Best Paper Award Winners

image of IEEE award

image of 3 EE faculty: Subhasish Mitra, Mary Wootters, and H.S. Philip Wong
January 2021

Professors Subhasish Mitra, H.S. Philip Wong, Mary Wootters, and their students recently published "Illusion of large on-chip memory by networked computing chips for neural network inference", in Nature.

Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called "memory wall" that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI.

"Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life," said Professor Subhasish Mitra.

The team has designed a system that can run AI tasks faster, and with less energy, by harnessing eight hybrid chips, each with its own data processor built right next to its own memory storage.

This paper builds on their prior development of a new memory technology, called RRAM, that stores data even when power is switched off – like flash memory – only faster and more energy efficiently. Their RRAM advance enabled the Stanford researchers to develop an earlier generation of hybrid chips that worked alone. Their latest design incorporates a critical new element: algorithms that meld the eight, separate hybrid chips into one energy-efficient AI-processing engine.

Additional authors are Robert M. Radway, Andrew Bartolo, Paul C. Jolly, Zainab F. Khan, Binh Q. Le, Pulkit Tandon, Tony F. Wu, Yunfeng Xin, Elisa Vianello, Pascal Vivet, Etienne Nowak, Mohamed M. Sabry Aly, and Edith Beigne.

[Excerpted from "Stanford researchers combine processors and memory on multiple hybrid chips to run AI on battery-powered smart devices"]

image of prof emeritus Martin E. Hellman
January 2021

Congratulations to Professor Emeritus Martin Hellman. He has been selected as a 2020 Association for Computing Machinery (ACM) Fellow.

The ACM Fellows program recognizes the top 1% of ACM Members for their outstanding accomplishments in computing and information technology and/or outstanding service to ACM and the larger computing community. Fellows are nominated by their peers, with nominations reviewed by a distinguished selection committee.

"This year our task in selecting the 2020 Fellows was a little more challenging, as we had a record number of nominations from around the world," explained ACM President Gabriele Kotsis. "The 2020 ACM Fellows have demonstrated excellence across many disciplines of computing. These men and women have made pivotal contributions to technologies that are transforming whole industries, as well as our personal lives. We fully expect that these new ACM Fellows will continue in the vanguard in their respective fields."

 

Excerpted from ACM.org's "2020 ACM Fellows Recognized for Work that Underpins Today's Computing Innovations".

 

Please join us in congratulating Marty for this well-deserved recognition.

 

Related News

 

image of prof H. Tom Soh
January 2021

EE Professor Tom Soh, in collaboration with Professor Eric Appel, and colleagues have developed a technology that can provide real time diagnostic information. Their device, which they've dubbed the "Real-time ELISA," is able to perform many blood tests very quickly and then stitch the individual results together to enable continuous, real-time monitoring of a patient's blood chemistry. Instead of a snapshot, the researchers end up with something more like a movie.

"A blood test is great, but it can't tell you, for example, whether insulin or glucose levels are increasing or decreasing in a patient," said Professor Tom Soh. "Knowing the direction of change is important."

In their recent study, "A fluorescence sandwich immunoassay for the real-time continuous detection of glucose and insulin in live animals", published in the journal Nature Biomedical Engineering, the researchers used the device to simultaneously detect insulin and glucose levels in living diabetic laboratory rats. But the researchers say their tool is capable of so much more because it can be easily modified to monitor virtually any protein or disease biomarker of interest.

Authors are PhD candidates Mahla Poudineh, Caitlin L. Maikawa, Eric Yue Ma, Jing Pan, Dan Mamerow, Yan Hang, Sam W. Baker, Ahmad Beirami, Alex Yoshikawa, researcher Michael Eisenstein, Professor Seung Kim, and Professor Jelena Vučković.

Technologically, the system relies upon an existing technology called Enzyme-linked Immunosorbent Assay – ELISA ("ee-LYZ-ah") for short. ELISA has been the "gold standard" of biomolecular detection since the early 1970s and can identify virtually any peptide, protein, antibody or hormone in the blood. An ELISA assay is good at identifying allergies, for instance. It is also used to spot viruses like HIV, West Nile and the SARS-CoV-2 coronavirus that causes COVID-19.

The Real-time ELISA is essentially an entire lab within a chip with tiny pipes and valves no wider than a human hair. An intravenous needle directs blood from the patient into the device's tiny circuits where ELISA is performed over and over.

 Excerpted from "Stanford researchers develop lab-on-a-chip that turns blood test snapshots into continuous movies", December 21, 2020.

Related News

Pages

January

No content classified for this term

February

February 2014

Three staff members each received a $50 Visa card in recognition of their extraordinary efforts as part of the department’s 2014 Staff Gift Card Bonus Program. The EE department received several nominations in January, and nominations from 2013 were also considered.

Following are January’s gift card recipients and some of the comments from their nominators:

Ann Guerra, Faculty Administrator

  • “She is very kind to students and always enthusiastic to help students… every time we need emergent help, she is willing to give us a hand.”
  • “Ann helps anyone who goes to her for help with anything, sometimes when it’s beyond her duty.” 

Teresa Nguyen, Student Accounting Associate

  • “She stays on top of our many, many student financial issues, is an extremely reliable source of information and is super friendly.”
  • “Teresa’s cheerful disposition, her determination, and her professionalism seem to go above and beyond what is simply required.”

Helen Niu, Faculty Administrator

  • “Helen is always a pleasure to work with.”
  • “She goes the extra mile in her dealings with me, which is very much appreciated.”

The School of Engineering once again gave the EE department several gift cards to distribute to staff members who are recognized for going above and beyond. More people will be recognized next month, and past nominations will still be eligible for future months. EE faculty, staff and students are welcome to nominate a deserving staff person by visitinghttps://gradapps.stanford.edu/NotableStaff/nomination/create.

Ann Guerra  Teresa Nguyen  Helen Niu

Pages

March

No content classified for this term

April

No content classified for this term

May

No content classified for this term

June

No content classified for this term

July

No content classified for this term

August

No content classified for this term

September

No content classified for this term

October

No content classified for this term

November

No content classified for this term

December

No content classified for this term

Story

No content classified for this term

Stanford

No content classified for this term

Test

No content classified for this term

Subscribe to RSS - News